OIAR (Ontology Integration with Alignment Reuse)

OIAR is an approach for creating a new ontology resulting from a simple merge of multiple ontologies using pairwise alignments between them. It automatically customizes and merges multiple large ontologies in a holistic way and in very short times.

Installation

Download OIAR and open it in your IDE, then manually add all the jars of the lib folder to the classpath. The lib folder contains OWL API 3.5 jars and their dependencies, Alignment API 4.9 jars, HermiT ontology reasoner jars, and ELK ontology reasoner jars. Then, run one of the three following classes of the src/integration folder (depending on your choice) :

• AutomaticIntegrationWithoutRefactoring : It uses the predefined OWLOntologyMerger() method of OWL API, which automatically aggregates/concatenates the axioms of the loaded input ontologies. Then, it translates correspondences of the input alignments into bridging axioms, and adds them in order to link different entities.

• IntegrationWithoutRefactoring : It parses all the input ontologies' axioms, and creates an exact copy of them. Then, it adds bridging axioms of the input alignments in order to link different entities.

• IntegrationWithRefactoring : It parses all the input ontologies' axioms, and creates a refactored copy of them. A full IRI of an entity (which can be a class, a property, or an individual) is composed of a prefix, followed by a local name (i.e., a suffix or an abbriviated name). Refactoring consists in replacing the prefix IRIs of all entities (i.e., the part before the #) by the IRI of the furture merged ontology. Then, it adds bridging axioms of the input alignments in order to link different refactored entities.

N.B: Git should be installed in the machine, in order to run these algorithms directly from the downloaded project.

N.B: See OIAR (V2) if you want to try a version that uses OWL API 4.5. And see OIAR (V3) if you want to try a version that uses OWL API 5.1.

Input

• Two or more OWL ontologies to be merged (owl files in the Input folder) (the path of the files should be correct !)
• One or more ontology alignments (in the Alignment API format) (rdf files in the Input folder) (the path of the files should be correct !)
• A new IRI for the output merged ontology
• A threshold real value between [0,1] to filter the alignment correspondences by their confidence measure.

Output

• A new merged ontology (an owl file in the Output folder).

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Example

Let's merge the three ontologies of the Large Biomedical Ontologies OAEI track. We will merge them using reference alignments between all possible ontology pairs. This will ensure a complete semantic interoperability between them. All tests were performed with a confidence threshold equal to 0.0, so we have kept all correspondences / cells of the input alignments.

Input Alignments (FMA-NCI + SNOMED-NCI + FMA-SNOMED)

Here are three equivalence correspondences extracted from the three "Large Biomedical Ontologies" OAEI reference alignments. They match the Abdominal_lymph_node class (from FMA) to other classes (from NCI and SNOMED).

FMA-NCI

The first correspondence (in FMA-NCI) matches the Abdominal_lymph_node class (from FMA) to the Intra-abdominal_Lymph_Node class (from NCI):

FMA-SNOMED

The second correspondence (in FMA-SNOMED) matches the Abdominal_lymph_node class (from FMA) to the Abdominal_lymph_node_structure class (from SNOMED):

The third correspondence (in FMA-SNOMED) matches the Abdominal_lymph_node class (from FMA) to the Abdominal_lymph_node_group class (from SNOMED):

Input Ontologies (FMA + NCI + SNOMED)

FMA

Here is the definition/description of Abdominal_lymph_node class in its original ontology (FMA (Ont1)) :

Output Merged Ontology

The above-mentioned correspondences will lead to the addition of three equivalence axioms between the Abdominal_lymph_node class (from FMA (Ont1)) and its three matched classes Intra-abdominal_Lymph_Node (from NCI (Ont2)), Abdominal_lymph_node_group (from SNOMED (Ont3)), and Abdominal_lymph_node_structure (from SNOMED (Ont3)). In other words, these correspondences will lead to three bridging axioms added to the description of the class Abdominal_lymph_node. For this example, we have chosen to give our future merged ontology the following IRI : "http://integration".

The following figures show the Abdominal_lymph_node class in our output ontology that resulted from the merging of the three LargeBio ontologies. Axioms framed in red are the added bridging axioms.

You can view and download our merged ontologies from the Output folder.

Non-Refactored Version

For the non-customized version, axioms are identical to the original ones, and bridging axioms are added to them. You can download and view this non-refactored merged ontology from the Output folder.

Refactored Version

For the customized version, axioms are like the original ones, except that the IRIs of the all mentioned entities are customized and bridging axioms are added to them. You can download and view this refactored merged ontology from the Output folder.

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Conclusion

• Our final ontology is complete, in the sense that it retains all entities, axioms and hierarchies from the input ontologies, and all correspondences from the input alignments.
• Running OIAR for merging the Large Biomedical Ontologies does not exceed one minute.